Towards CNN Representations for Small MS Spectral Data Classification: From Transfer Learning to Cumulative Learning- Data

Name: Towards CNN Representations for Small MS Spectral Data Classification: From Transfer Learning to Cumulative Learning- Data
Creator: Khawla Seddiki
Published: 2020-09-27T16:25:34.484Z
Keywords: Mass Spectrometry, Lipidomics, Matrix-Assisted Laser Desorption-Ionization, Imaging Mass Spectrometry

Seddiki, Khawla; Saudemont, Philippe; Precioso, Frederic; Ogrinc, Nina; Wisztorski, Maxence; Salzet, Michel; FOURNIER, Isabelle; Droit, Arnoud

doi:10.17632/33cbb37cs2.1

Towards CNN Representations for Small MS Spectral Data Classification: From Transfer Learning to Cumulative Learning- Data

Published: 27 September 2020| Version 1 | DOI: 10.17632/33cbb37cs2.1

Contributors:

Khawla Seddiki, Philippe Saudemont, Frederic Precioso, Nina Ogrinc, Maxence Wisztorski, Michel Salzet, Isabelle FOURNIER, Arnoud Droit

Description

The databanks include raw files from beef liver and microorganisms acquired with the Water-Assisted Laser-Desorption Ionization - SpiderMass technology and a MALDI rat brain tissue image analysed with the Rapiflex (Bruker) at 50 x 50 um resolution in positive ion mode. The SpiderMass is composed of a remote IR laser ablation system and a Synapt G2s from Waters. Each databank consists of raw files, ReadMe file (explaining experimental conditions) and a sample list

Towards CNN Representations for Small MS Spectral Data Classification: From Transfer Learning to Cumulative Learning- Data

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